{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Bootstrap for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Bootstrap for Subsurface Data Analytics in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of bootstrap for subsurface modeling workflows. This should help you get started with building subsurface models that integrate uncertainty in the sample statistics.  \n",
    "\n",
    "#### Bootstrap\n",
    "\n",
    "Uncertainty in the sample statistics\n",
    "* one source of uncertainty is the paucity of data.\n",
    "* do 200 or even less wells provide a precise (and accurate estimate) of the mean? standard deviation? skew? P13?\n",
    "\n",
    "Would it be useful to know the uncertainty in these statistics due to limited sampling?\n",
    "* what is the impact of uncertainty in the mean porosity e.g. 20%+/-2%?\n",
    "\n",
    "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n",
    "\n",
    "Assumptions\n",
    "* sufficient, representative sampling, identical, idependent samples\n",
    "\n",
    "Limitations\n",
    "1. assumes the samples are representative \n",
    "2. assumes stationarity\n",
    "3. only accounts for uncertainty due to too few samples, e.g. no uncertainty due to changes away from data\n",
    "4. does not account for boundary of area of interest \n",
    "5. assumes the samples are independent\n",
    "6. does not account for other local information sources\n",
    "\n",
    "The **Bootstrap Approach** (Efron, 1982)\n",
    "\n",
    "Statistical resampling procedure to calculate uncertainty in a calculated statistic from the data itself.\n",
    "* Does this work?  Prove it to yourself, for uncertainty in the mean solution is standard error: \n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_\\overline{x} = \\frac{\\sigma^2_s}{n}\n",
    "\\end{equation}\n",
    "\n",
    "Extremely powerful - could calculate uncertainty in any statistic!  e.g. P13, skew etc.\n",
    "* Would not be possible access general uncertainty in any statistic without bootstrap.\n",
    "* Advanced forms account for spatial information and sampling strategy (game theory and Journel’s spatial bootstrap (1993).\n",
    "\n",
    "Steps: \n",
    "\n",
    "1. assemble a sample set, must be representative, reasonable to assume independence between samples\n",
    "\n",
    "2. optional: build a cumulative distribution function (CDF)\n",
    "    * may account for declustering weights, tail extrapolation\n",
    "    * could use analogous data to support\n",
    "\n",
    "3. For $\\ell = 1, \\ldots, L$ realizations, do the following:\n",
    "\n",
    "    * For $i = \\alpha, \\ldots, n$ data, do the following:\n",
    "\n",
    "        * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n",
    "\n",
    "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n",
    "\n",
    "7. Compile and summarize the $L$ realizations of the statistic of interest.\n",
    "\n",
    "This is a very powerful method.  Let's try it out.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data_biased.csv at https://git.io/fh0CW\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "import seaborn as sns                     # advanced statistical methods and plots\n",
    "from scipy import stats                   # summary statistics\n",
    "import math                               # trig etc.\n",
    "import scipy.signal as signal             # kernel for moving window calculation\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"c:/PGE383\")                     # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('sample_data_biased.csv') # load our data table\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop some samples so that we increase the variations in bootstrap samples for our demonstration below.  \n",
    "\n",
    "* We will resample 25% of the original data file.  \n",
    "\n",
    "* I use a random number seed so that we all get the same sample to work with.  \n",
    "\n",
    "```python \n",
    "df.sample(frac = 0.25, random_state = 73073)\n",
    "```\n",
    "\n",
    "where *frac* is the fraction to select and *random_state* is the random number seed.\n",
    "\n",
    "To get different random 25% subset of the data, just drop the 'rand_state' parameter or change the value from '73073' to any other integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 72 number of samples\n"
     ]
    }
   ],
   "source": [
    "df = df.sample(frac = 0.25, random_state = 73073) # extract 25% random samples to reduce the size of the dataset   \n",
    "print('Using ' + str(len(df)) + ' number of samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the DataFrame would be useful and we already learned about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       X    Y  Facies  Porosity         Perm\n",
      "150   20   69       0  0.104484     3.003225\n",
      "17   400  600       1  0.163054   317.883581\n",
      "68   640  529       1  0.120547    10.801778\n",
      "234  490  289       1  0.144525    16.251107\n",
      "214  470  899       1  0.211944  1302.937858\n"
     ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
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       "      <th>153</th>\n",
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       "      <td>0.134607</td>\n",
       "      <td>9.776276</td>\n",
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       "      <th>5</th>\n",
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       "      <td>106.491795</td>\n",
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       "      <th>170</th>\n",
       "      <td>670</td>\n",
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       "      <td>22.277121</td>\n",
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       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>890</td>\n",
       "      <td>489</td>\n",
       "      <td>0</td>\n",
       "      <td>0.103176</td>\n",
       "      <td>2.846036</td>\n",
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       "    <tr>\n",
       "      <th>159</th>\n",
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      ],
      "text/plain": [
       "       X    Y  Facies  Porosity         Perm\n",
       "150   20   69       0  0.104484     3.003225\n",
       "17   400  600       1  0.163054   317.883581\n",
       "68   640  529       1  0.120547    10.801778\n",
       "234  490  289       1  0.144525    16.251107\n",
       "214  470  899       1  0.211944  1302.937858\n",
       "153  360  279       1  0.134607     9.776276\n",
       "5    200  800       1  0.154648   106.491795\n",
       "170  670  889       1  0.119601    17.052309\n",
       "252  200  359       0  0.077625     0.540486\n",
       "131  320   99       1  0.141749    13.154686\n",
       "133  250  569       1  0.120868    22.277121\n",
       "245  890  489       0  0.103176     2.846036\n",
       "159  590   69       1  0.114117    13.251200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.iloc[0:5,:])                   # display first 4 samples in the table as a preview\n",
    "df.head(n=13)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>72.0</td>\n",
       "      <td>448.611111</td>\n",
       "      <td>239.825674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>72.0</td>\n",
       "      <td>495.111111</td>\n",
       "      <td>293.106026</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>716.750000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.375293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.135360</td>\n",
       "      <td>0.039304</td>\n",
       "      <td>0.074349</td>\n",
       "      <td>0.106374</td>\n",
       "      <td>0.124065</td>\n",
       "      <td>0.147035</td>\n",
       "      <td>0.223661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>72.0</td>\n",
       "      <td>242.573614</td>\n",
       "      <td>553.551806</td>\n",
       "      <td>0.093252</td>\n",
       "      <td>2.979039</td>\n",
       "      <td>13.509371</td>\n",
       "      <td>51.052217</td>\n",
       "      <td>2372.383732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean         std        min         25%         50%  \\\n",
       "X          72.0  448.611111  239.825674   0.000000  295.000000  415.000000   \n",
       "Y          72.0  495.111111  293.106026  39.000000  279.000000  494.000000   \n",
       "Facies     72.0    0.833333    0.375293   0.000000    1.000000    1.000000   \n",
       "Porosity   72.0    0.135360    0.039304   0.074349    0.106374    0.124065   \n",
       "Perm       72.0  242.573614  553.551806   0.093252    2.979039   13.509371   \n",
       "\n",
       "                 75%          max  \n",
       "X         640.000000   990.000000  \n",
       "Y         716.750000   999.000000  \n",
       "Facies      1.000000     1.000000  \n",
       "Porosity    0.147035     0.223661  \n",
       "Perm       51.052217  2372.383732  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Tabular Data with Location Maps \n",
    "\n",
    "It is natural to set the x and y coordinate and feature ranges manually. e.g. do you want your color bar to go from 0.05887 to 0.24230 exactly? Also, let's pick a color map for display. \n",
    "\n",
    "* I heard that plasma is known to be friendly to the color blind as the color and intensity vary together (hope I got that right, it was an interesting Twitter conversation started by Matt Hall from Agile if I recall correctly). \n",
    "\n",
    "We will assume a study area of 0 to 1,000m in x and y and omit any data outside this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0               # range of x values\n",
    "ymin = 0.0; ymax = 1000.0               # range of y values\n",
    "pormin = 0.05; pormax = 0.25;           # range of porosity values\n",
    "nx = 100; ny = 100; csize = 10.0\n",
    "cmap = plt.cm.plasma                    # color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out locmap. This is a reimplementation of GSLIB's locmap program that uses matplotlib. I hope you find it simpler than matplotlib.\n",
    "\n",
    "* If you want to get more advanced and build custom plots lock at the source. \n",
    "\n",
    "If you improve it, send me the new code. Any help is appreciated. \n",
    "\n",
    "* To see the parameters, just type the command name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.GSLIB.locmap(df, xcol, ycol, vcol, xmin, xmax, ymin, ymax, vmin, vmax, title, xlabel, ylabel, vlabel, cmap, fig_name)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can populate the plotting parameters and visualize the porosity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "GSLIB.locmap_st(df,'X','Y','Porosity',xmin,xmax,ymin,ymax,pormin,pormax,'Well Data - Porosity','X(m)','Y(m)','Porosity (fraction)',cmap)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Debiasing the Spatial Dataset\n",
    "\n",
    "We must mitigate bias in the spatial sampling.\n",
    "\n",
    "* If the samples are biased, then any resulting uncertainty model in a statistic may be biased.\n",
    "\n",
    "* Cell-based declustering is a common method for debiasing spatial data.\n",
    "\n",
    "Let's calculate some declustering weights. There is a demonstration on declustering here https://git.io/fhgJl if you need more information. \n",
    "\n",
    "* I have coded cell based declustering from GSLIB in the GeostatsPy package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 72 data with:\n",
      "   mean of      0.13535995545833332 \n",
      "   min and max  0.074348923 and 0.223660709\n",
      "   standard dev 0.03902973923480811 \n",
      "Declustered mean = 0.126 and declustered standard deviation = 0.032\n"
     ]
    }
   ],
   "source": [
    "wts, cell_sizes, dmeans = geostats.declus(df,'X','Y','Porosity',iminmax = 1, noff= 10, ncell=100,cmin=10,cmax=2000)\n",
    "df['Wts'] = wts                           # add weights to the sample data DataFrame\n",
    "df.head()                                 # preview to check the sample data DataFrame\n",
    "\n",
    "def weighted_avg_and_std(values, weights): # function to calculate weighted mean and st. dev., from Eric O Lebigot, stack overflow,\n",
    "    average = np.average(values, weights=weights)\n",
    "    variance = np.average((values-average)**2, weights=weights)\n",
    "    return (average, math.sqrt(variance))\n",
    "\n",
    "sample_avg, sample_stdev = weighted_avg_and_std(df['Porosity'],df['Wts'])\n",
    "print('Declustered mean = ' + str(round(sample_avg,3)) + ' and declustered standard deviation = ' + str(round(sample_stdev,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A Couple of Bootstrap Realizations\n",
    "\n",
    "We will attempt boostrap by-hand and manually loop over $L$ realizations and draw $n$ samples to calculate the summary statistics of interest, mean and variance. The choice function from the random package simplifies sampling with replacement from a set of samples with weights.\n",
    "\n",
    "This command returns a ndarray with k samples with replacment from the 'Porosity' column of our DataFrame (df) accounting for the data weights in column 'Wts'.\n",
    "```p\n",
    "samples1 = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "```\n",
    "\n",
    "It is instructive to look at a few of these realizations from the original declustered data set.\n",
    "\n",
    "**CodingTips** - To understand this code below there are just a couple of Python concepts that you need to add to your Python arsenal.\n",
    "\n",
    "1. declaring arrays - NumPy has a lot of great array (ndarray) functionality. There are build in functions to make a ndarray of any length (and dimension). This includes 'zeros', 'ones' and 'rand', so when we use this code: \n",
    "\n",
    " `samples = np.zeros((nreal,len(df))` \n",
    " \n",
    " we're making a 2D array of size number of realizations, nreal, x number of data in the DataFrame, df, pre-populated with zeros.  Note the len(df) command returns the number of samples in the DataFrame. \n",
    "\n",
    "\n",
    "2. For Loops - when we are using the command below, we are instructing the computer to loop over all the indented code below the command for $ireal = 0,1,2,\\ldots,nreal-1$ times. For each loop the $ireal$ variable increments, so we can use this to save each result to a different index in the arrays mean and stdev. Note, Python arrays index starting at 0 and stop at the length - 1.\n",
    "\n",
    " `for ireal in range(0,nreal):`\n",
    "\n",
    " we are running each bootstrap resampled realization, and storing each realization in a row of the 2D array (for a 2D array the first index is the row index and the second index is the column index).\n",
    " \n",
    "    \n",
    "3. Subplots - we can put multple plots together with subplots.  To do this we specify\n",
    "\n",
    " `plt.subplot(3,3,1)`\n",
    "\n",
    " before each plot.  Where the first value is the number of row, the second number is the number of columns and the third number is the image index from $0,...,nrow x ncol$.  The images sort from left to right and then from top to bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nreal = 200                               # number of bootstrap realizations\n",
    "nhist = 8                                 # number of bootstrap histograms to view (max 8)                             \n",
    "samples = np.zeros((nreal,len(df))); real_label = []\n",
    "\n",
    "for ireal in range(0,nreal):\n",
    "    samples[ireal] = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "GSLIB.hist_st(df['Porosity'],pormin,pormax,False,False,20,df['Wts'],'Porosity (fraction)','Declustered Porosity')\n",
    "\n",
    "for ireal in range(0,nhist):\n",
    "    plt.subplot(3,3,ireal+2)\n",
    "    GSLIB.hist_st(samples[ireal],pormin,pormax,False,False,20,None,'Porosity (fraction)','Bootstrap Porosity Realization ' + str(ireal+1))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=3.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Shape\n",
    "\n",
    "The distribution seems to be bimodel (two peaks).  \n",
    "\n",
    "* Instead of looking at a few histograms of bootstrap realizations, let's look at many realizations and see if there is always, mostly or just sometimes two modes.\n",
    "\n",
    "We will visualize many bootstrap realizations of the entire distribution.\n",
    "\n",
    "This code loops over the realizations and adds each to a probability density plot:\n",
    "\n",
    "```python\n",
    "for ireal in range(0,nreal):              \n",
    "    sns.distplot(samples[ireal])\n",
    "```\n",
    "\n",
    "where each bootstrap realization is a row in the 2D ndarray samples.\n",
    "\n",
    "The additional prameters control the visualization:\n",
    "\n",
    "```python\n",
    "color = 'black', hist = False, kde = True,kde_kws = {\"alpha\": 0.02, 'linewidth': 5}\n",
    "```\n",
    "for example: \n",
    "\n",
    "* **kde** is a kernel density estimator to conver the data to a smooth density estimate\n",
    "\n",
    "* **alpha** is the level of transparency\n",
    "\n",
    "See what you think.  From the image below can you see the uncertainty in the distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ireal in range(0,nreal):              # loop over the realizations and plot with seaborn package\n",
    "    # Draw the density plot\n",
    "    # sns.distplot(samples[ireal], color = 'black', hist = False, kde = True,kde_kws = {\"alpha\": 0.002, 'linewidth': 5})\n",
    "    sns.kdeplot(samples[ireal], color = 'black',alpha=0.6,lw=.1)\n",
    "plt.title('Bootstrap Porosity Distribution - Uncertainty in the Distribution Shape')\n",
    "plt.xlabel('Porosity (fraction)')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the bootstrap distributions vary quite a bit from the original, but the bimodal shape is consistent; therefore the bimodal distribution form is **not due to random effect / sparse sampling**! We are likely dealing with two distinct processes and / or spatial trends.\n",
    "\n",
    "#### Summarizations Over Bootstrap Realizations\n",
    "\n",
    "Let's make a loop to conduct $L$ resamples and calculate the average and standard deviation for each  ($m^\\ell$, $\\sigma^2_{\\ell}$,  for $\\ell = 0,\\dots,L-1$). We then summarization over these $L$ realizations.  \n",
    "\n",
    "I did not find any built-in, concise functions to accomplish this, i.e. with a single line of code, so we are going to do it by hand.  \n",
    "\n",
    "To understand this code there are just a couple of Python concepts that you need to add to your Python arsenal.\n",
    "\n",
    "1. declaring arrays - NumPy has a lot of great array (ndarray) functionality. There are build in functions to make a ndarray of any length (and dimension). This includes 'zeros', 'ones' and 'rand', so when we use this code: \n",
    "\n",
    " `mean = np.zeros(L); stdev = np.zeros(L)`\n",
    "\n",
    " we're making arrays of length $L$ pre-populated with zeros.\n",
    "\n",
    "2. For Loops - when we are using the command below, we are instructing the computer to loop over all the indented code below the command for $l = 0,1,2,\\ldots,L-1$ times. For each loop the $l$ variable increments, so we can use this to save each result to a different index in the arrays mean and stdev. Note, Python arrays index starting at 0 and stop at the length - 1.\n",
    "\n",
    " `for l in range(0, L):` \n",
    " \n",
    " we are running each bootstrap resampled realization, calculating the average and standard deviation and storing them in the arrays that we already declared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Bootstrap Porosity Average:\n",
      "DescribeResult(nobs=20000, minmax=(0.11189613161111112, 0.1427788398472222), mean=0.12574515073763054, variance=1.4551639442818001e-05, skewness=0.14421649944841558, kurtosis=0.0502383438489713)\n",
      "\n",
      "Uncertainty in Porosity Average: P10 12.1%, P50 12.6%, P90 13.1%\n",
      "\n",
      "Summary Statistics for Bootstrap Porosity Standard Deviation:\n",
      "DescribeResult(nobs=20000, minmax=(0.01837565304924646, 0.04411920738419295), mean=0.03192042940119679, variance=1.1162327844080094e-05, skewness=-0.1306329821565926, kurtosis=-0.015352650563098091)\n",
      "\n",
      "Uncertainty in Porosity Standard Deviation: P10 2.8000000000000003%, P50 3.2%, P90 3.6%\n"
     ]
    }
   ],
   "source": [
    "L = 20000                                  # set the number of realizations for uncertainty calculation\n",
    "mean = np.zeros(L); stdev = np.zeros(L)    # declare arrays to hold the realizations of the statistics\n",
    "P10 = np.zeros(L)                          \n",
    "P50 = np.zeros(L); P90 = np.zeros(L)                         \n",
    "for l in range(0, L):                      # loop over realizations\n",
    "    samples = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "    mean[l] = np.average(samples)\n",
    "    stdev[l] = np.std(samples)\n",
    "    \n",
    "plt.subplot(121)\n",
    "GSLIB.hist_st(mean,0.11,0.15,False,False,50,None,'Average Porosity (fraction)','Bootstrap Uncertainty in Porosity Average')\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.hist_st(stdev,0.015,0.045,False,False,50,None,'Standard Deviation Porosity (fraction)','Bootstrap Uncertainty in Porosity Standard Deviation')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()   \n",
    "    \n",
    "print('Summary Statistics for Bootstrap Porosity Average:')\n",
    "print(stats.describe(mean))\n",
    "print('\\nUncertainty in Porosity Average: P10 ' + str(round(np.percentile(mean,10)*100,1)) + '%, P50 ' + str(round(np.percentile(mean,50)*100,1)) + '%, P90 ' + str(round(np.percentile(mean,90)*100,1)) +\"%\") \n",
    "\n",
    "print('\\nSummary Statistics for Bootstrap Porosity Standard Deviation:')\n",
    "print(stats.describe(stdev)); \n",
    "print('\\nUncertainty in Porosity Standard Deviation: P10 ' + str(round(np.percentile(stdev,10),3)*100) + '%, P50 ' + str(round(np.percentile(stdev,50),3)*100) + '%, P90 ' + str(round(np.percentile(stdev,90)*100,1)) + \"%\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uncertainty in the Percentiles\n",
    "\n",
    "We could even calculate the uncertainty in the percentiles, well-based P10, P50 and P90.\n",
    "\n",
    "* this demonstrates the flexiblity of the bootstrap method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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   "source": [
    "L = 20000                                  # set the number of realizations for uncertainty calculation\n",
    "mean = np.zeros(L); stdev = np.zeros(L)    # declare arrays to hold the realizations of the statistics\n",
    "P10 = np.zeros(L)                          \n",
    "P50 = np.zeros(L); P90 = np.zeros(L)                         \n",
    "for l in range(0, L):                      # loop over realizations\n",
    "    samples = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "    P10[l] = np.percentile(q = 10, a = samples)\n",
    "    P50[l] = np.percentile(q = 50, a = samples)\n",
    "    P90[l] = np.percentile(q = 90, a = samples)\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.hist(P10, facecolor='red',bins=np.linspace(0.05,0.25,50),alpha=0.2,density=False,edgecolor='black',label='P10')\n",
    "plt.hist(P50, facecolor='blue',bins=np.linspace(0.05,0.25,50),alpha=0.2,density=False,edgecolor='black',label = 'P50')\n",
    "plt.hist(P90, facecolor='green',bins=np.linspace(0.05,0.25,50),alpha=0.2,density=False,edgecolor='black',label = 'P90')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,10000.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Bootstrap Uncertainty in Porosity Well-based P10, P50 and P90 ')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
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    "#### Comments\n",
    "\n",
    "This was a basic demonstration of bootstrap. Much more could be done, you could replace the statistics, average and standard deviation with any other statistics, for example P90, kurtosis, P13 etc. I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin"
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